Understanding Signal Optimization

Signal optimization improves the quality of data your advertising platforms use to find new customers. Better signals mean better targeting and lower acquisition costs.

What are signals?

Ad platforms like Google and Meta use machine learning to decide who sees your ads. The quality of their decisions depends on the signals they receive — data points about who your customers are, what they buy, and how they engage.

When these signals are polluted with existing customer data, the algorithms optimize for the wrong thing: finding people similar to your existing customers who are easy to convert, rather than genuinely new prospects.

How WasteNot improves your signals

By excluding existing customers from your prospecting campaigns, WasteNot ensures the conversion data flowing back to ad platforms reflects actual new customer acquisitions. This means:

• Lookalike audiences are built from genuinely new customer conversions
• Smart bidding optimizes for real acquisition events, not repeat purchases
• Platform algorithms learn what a true new customer looks like

The compounding effect

Signal optimization creates a virtuous cycle:

1. Cleaner conversion data flows to your ad platforms
2. Algorithms build better models of your ideal new customer
3. Targeting improves, bringing in higher-quality prospects
4. Conversion rates increase while acquisition costs decrease
5. Even cleaner data flows back — and the cycle continues

Getting started

Signal optimization happens automatically when you use WasteNot to manage your exclusion audiences. The key steps are:

1. Connect your data sources
2. Build audiences and sync them to your ad platforms
3. Let the platforms re-optimize with cleaner signals
4. Monitor improvements in your performance reports